Trends in toxicological findings in unintentional opioid or stimulant toxicity deaths in Québec, Canada, 2012–2021: Has Québec entered a new era of drug‐related deaths?
Bibliographic record
Abstract
INTRODUCTION: We aimed to describe rates and toxicological findings of unintentional opioid and stimulant toxicity deaths, 2012-2021. METHODS: The dataset included accidental deaths determined by the Coroner to be due to opioids or stimulants. We calculated annual crude mortality rates and described combinations of drugs identified in toxicological examinations of these deaths. We described temporal trends in the detection of specific opioids, stimulants, benzodiazepines (including novel benzodiazepines), gabapentinoids and z-drugs in deaths due to opioids and stimulants. RESULTS: Mortality rates increased over time, reaching their peak in 2020 and remaining high in 2021. In deaths due to opioids, there was a decline in the proportion of deaths involving pharmaceutical opioids after 2019, and a corresponding increase in the proportion of deaths with fentanyl detected. Benzodiazepines were often present in deaths due to opioids, with novel benzodiazepines increasing rapidly from 2019 onwards. Cocaine was the most frequently detected drug in deaths due to stimulants, but amphetamine/methamphetamine was detected in around half of all stimulant deaths from 2016 onwards. DISCUSSION AND CONCLUSIONS: Despite availability of a multitude of overdose prevention interventions, mortality rates due to drug toxicity have increased in Québec. Toxicological findings of these deaths suggest concerning shifts in the illicit drug market, with Québec potentially having entered a new era of elevated overdose mortality. Intervention scale-up is essential, but unlikely to be sufficient, to reduce drug-related mortality. Policy reform to address the root causes of drug toxicity deaths, including an unpredictable drug supply, strained health systems and socio-economic precarity, is essential.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".